Coronavirus disease (COVID-19), which is caused by SARS-CoV-2, has been announced as a global pandemic by the World Health Organization (WHO), which results in the collapsing of the healthcare systems in several countries around the globe. Machine learning (ML) methods are one of the most utilized approaches in artificial intelligence (AI) to classify COVID-19 images. However, there are many machine-learning methods used to classify COVID-19. The question is: which machine learning method is best over multi-criteria evaluation? Therefore, this research presents benchmarking of COVID-19 machine learning methods, which is recognized as a multi-criteria decision-making (MCDM) problem. In the recent century, the trend of developing

     This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being  0.66975075, 0.470

Breast cancer is the commonest cause of cancer related death in women worldwide. Amplification or over-expression of the ERBB2 (HER/neu) gene occurs in approximately 15-30% of breast cancer cases and it is strongly associated with an increased disease recurrence and a poor prognosis. Determination of HER2/neu status is crucial in the treatment plan as that positive cases will respond to trastuzumab therapy. It has been used to test for HER2/neu by immunohistochemistry as a first step and then to study only the equivocal positive cases (score 2+) by in situ hybridization technique. The aim of our study is to compare between immunohistochemistry and silver in situ hybridization (SISH) in assessment of human epidermal growth factor (HER2/neu)

The aim of this research was to indicate the opinion of the Iraqi consumer awareness of the risks associated with consuming canned food, the questionnaire was included 20 questions for label information, consumer culture, shopping, marketing, awareness and knowledge as a tool to survey the opinions of 300 consumers in Baghdad, the data was analyzed by using percentage, weighted mean, and weight percent, the results obtained showed that the Iraqi consumer need more information, training and guidance programs in food safety handling issue for canned food, especially in analysis of label information and growing of consumer culture for shopping, right marketing, awareness and knowledge.

    In this survey, there are 14 species belonging to 14 genera, nine families and two orders, collected on Macrofungi from Tikrit city, Salahadin Governorate, North Central of Iraq. The members of Coleoptera were more abundant than flies on Macrofungi.

    The family of Ciidae and Leiodidae (Order, Coleoptera), Mycetophilidae (Order, Diptera), and 6 species are recorded for the first time for insect fauna of Iraq.